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Cost Analysis for Service-oriented Holonic Manufacturing System Design
Proceedings of the 14th IFAC Symposium on Information Control Problems in Manufacturing Bucharest, Romania, May 23-25, 2012
Cost Analysis for Service-oriented Holonic Manufacturing System Design Cristina Morariu, Octavian Morariu, Theodor Borangiu
University Politehnica of Bucharest, Dept. of Automation and Applied Informatics e-mail: {cristina.morariu, octavian.morariu, theodor.borangiu}@cimr.pub.ro Abstract: This paper presents a formal methodology to perform a cost analysis for the customer order management (COM) process in the context of holonic manufacturing systems. The business process described in the paper, centred on COM, interconnects reference architecture with several enhancements that help in the mapping of the financial relevant activities. The process is analysed from two perspectives, first to show how the resource break-down rate in the HMES influences the overall profit and secondly to show the relationship between the percentage of products that fail the quality assurance test and the overall profit. The process was simulated using a BPM tool and the results are discussed. Keywords: HMES design, SOA, business services, cost of quality, Business Process Management
1. INTRODUCTION The environment of the manufacturing industry is nowadays characterized by radical transformation, concerning value creation. Value is no longer created simply in the production process, but derived from the knowledge, innovation, and service embodied throughout the entire product life cycle and their relationship with customers. The fundamental change factors in manufacturing - customers, market conditions and environmental constraints - will determine the economic growth engines for the business of the future, based on: • Customized solutions: integrating capabilities through products, services, and information to meet individual customer requirements. • Environmental sustainability: minimizing waste, energy and resource utilization. • Market conditions: creating individual solutions in a costeffective way to sustain a profitable business enterprise. • Timeliness: instant delivery of service to customers-product development, production lead times, delivery/ servicing time lines, and investment horizons are compressed. Businesses in the manufacturing industry only succeed if they meet all three of these conditions. Manufacturing will compete on delivering customer value at more and more lower costs, which will require individual products to be designed, planned and executed for individual customers to meet individual needs. Competitive advantage will be determined by production to individual specifications, and competitive cost structures should be put in place in order to ensure that customization is commercially feasible. The ultimate goal will be the competitive "batch of one". Mass customization will entail business tailoring product functionality, design and service to satisfy individual customer requirements, but also making differentiated products at high speed and in high volumes to keep unit costs at a minimum. Therefore, manufacturing enterprises will have to improve flexibility through continuous innovation and shorter production runs that can respond with agility to
978-3-902661-98-2/12/$20.00 © 2012 IFAC
changing and more specialized customer requirements together with shorter product life cycles. Mass customization will require further changes within the business of manufacturing: • Information systems integrating at enterprise level business and technical processes, that can quickly identify customer specifications, provide pertinent cost analysis, turn them automatically into work orders, and pull systems throughout supply chains and product-driven fabrication processes that meet both high-dynamics customer demands and manufacturing requirements influenced by the work environment, i.e. on demand production. • Agile production facilities for product planning, scheduling, transport, processing, assembling and quality control that can be rapidly reconfigured rather than reprogrammed, to produce different products or support product variations with minimal changeover times. • Interoperability, through standard product platforms and easily interchangeable and compatible physical devices, information systems and communication interfaces. • Process-based quality control and certification systems. • Traceability through individualized tracking and management of feature-based material flows. • Lean, just-in-time supply chains and low-cost logistics infrastructure. • Customer service that cover the life cycle of products, from design to financing, after-sale service, maintenance, recycling and disposal. In this context, the service value creation model (VCM) at manufacturing enterprise level consists into using a Service Component Architecture for business process applications, based on entities which handle (ask for, discover, provide, monitor) services. In this componentization view, a service is a piece of software encapsulating the business / control logic or resource functionality of an entity that exhibits an individual competence and responds to a specific request to fulfil a local (product operation, verification) or global objective (batch production).
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Current research is focusing on ways to integrate and map the Manufacturing Execution System (MES) to Service Oriented Architecture (SOA) concepts (Borangiu 2011). Modern enterprises are transforming their business models to a SOA oriented model as described in Forrester research (Forrester Research 2005), in order to reduce costs and to formalize and optimize the existing business processes.
• Business or Enterprise Applications such as those found in ERP for financial reporting, order management, etc. • MES applications that coordinate production and integrate with the plant floor and with enterprise level applications. • Device layer applications such as distributed control system or supervisory control and data acquisition (SCADA) which are used to manage the device layer in manufacturing.
2. SOA IN MANUFACTURING
The PROSA reference architecture (Van Brussel et al. 1998) for holonic manufacturing was adopted for the Holonic MES (HMES) developed in the present research, extended with the auto-supply functionality; the basic holons, defined as order / supply holon (OH/SH), resource holon (RH), product holon (PH) are coordinated by an expertise holon (EH) in the centralized control mode (Borangiu et al. 2011).
Enterprise Service Composition Environment
The way how service orientation is used in manufacturing is shown in Fig. 1, which gives an architectural view of the 5 business layers separating the enterprise (corresponding to the ISA-95 Enterprise Domain Hierarchy) (ISA-95 2007): Portal / Dashboard layer: • Data Aggregation & Visualization • User / Role based interaction
The reported research is related to the service-oriented link between the Plant HMES Systems (HMES) and one of the composite business processes - Customer Order Management (COM), aiming at enterprise integration with respect to both regular batch orders and rush orders (Fig. 2).
Composite Business Processes
Customer Order Inventory & Asset Supplier Management Integration Management
Offer Request Management
Enterprise business
Business Services layer [WSDL wraps business applications]
…
Componentization
Activity Monitoring
4
Customer Order Management
2
3
HMES
Products
Fig. 2. COM integration with HMES Data collection BAO
ERP SCM, CRM
The customer order management module receives the product order from the customer (via the Customer Relationship Management - CRM system) and checks if the HMES can complete it in due time and with which cost, based on resource status and availability. In this context, the COM module, and more specifically the business process orchestrating the actions associated with the processing of the customer order, encapsulates some of the financially relevant aspects of manufacturing.
Service Registry, WIP Tracking
Integration layer: Enterprise Service Bus (ESB)
PLM EDW
1
Order completed
…
Service Enablement BPM
OH sequence
Customer Order
Business choreography of SOA
Master Data Management
Plant HMES Systems SCADA Systems
Fig. 1. Overall SOA for manufacturing The bottom layer consists of existing or legacy applications that provide the foundation for how the business’ data is used. These are the ‘mission critical’ applications that keep the business running on a day to day basis. The next layer provides the integration. In a SOA, the integration layer is realized with Enterprise Services Bus (ESB). The ESB layer provides security, transport, mediation and event services. It also can provide business metrics and a workflow or business process engine. The Business Services (middle) layer is an abstraction layer of services "fronting" the foundation IT systems. These services are what do the work within the SOA. These services are represented using Web Service Description Language (WSDL) that wraps the business applications. The Business Processes layer consists of business processes that are created by combining the services in the Business Services layer together to create composite applications. Composite applications are a new way to develop application within the SOA. The Portal / Dashboard layer consists of data aggregation and visualization. The enterprise as a whole can be represented as layers under the control of three basic types of applications:
In this paper we will propose a business process for COM, based on the main HMES processes: batch product planning, operation scheduling and resource allocation, fed with shop floor asset data (fabrication year, functions, cross-redundancy and Mean-Time-Between-Failures) and having associated computing models for cost functions like production cost, resource maintenance, quality control, product delivery and revenue. Based on this process a set of analyses are described, showing how the resource break-down rate is influencing the profit, how the used scheduling algorithm influences the profit variation and how the percentage of products that pass the quality test influences the overall profit. Answers are given to the questions: "What is the optimum percentage of resource break-downs considering a profit margin and the related maintenance costs?"; "How affects the scheduling algorithm the profit?"; "How affects the profit the percentage of products which don't pass the QA (Quality Assurance) tests?" Chapter 3 focuses on financial aspects used in the process analysis. Chapter 4 describes the business process proposed. In chapter 5 the experimental method for process analysis is described and the results are presented.
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3. FINANCIAL ASPECTS IN COST ANALYSIS The business process proposed for customer management order is represented in a simplified way in Fig. 3.
Maintainability (Mean-Time-To-Repair MTTR): this is the measure of the ability of a unit of equipment to be restored to its performance capability within a specified average time. The relation between reliability and maintainability is given by the impact of increased maintenance procedures on the shape of the probability density function. The assumption is that proactive maintenance procedures will decrease the failure probability and will thus increase the reliability of the resources to a certain degree. In the HMES context, resource break-down rate can be defined as a measure of reliability for individual critical resources.
Fig. 3. Customer Order Management process In the COM business process three checks are performed that can alter the paths followed by the process, thus influencing the overall profit generated by the system. The first check is related to availability of the physical resources required for completing the order, the second asks for the possibility to schedule the order in the due delivery date and the third one is done for quality assurance before the product is delivered. For each of these three process branches a probability can be defined and associated with each decision path that will be discussed from a cost / profit perspective. In order to analyse the costs associated with the physical resources involved in the manufacturing process, two metrics are defined (PMBOK 2000): Reliability (Mean-Time-Between-Failures MTBF): is the degree to which a unit of equipment is performing its intended function under specified conditions for a specified period of time. There are two approaches to compute reliability: the first predicts the MTBF using a mathematical approach, aggregating the MTBF of components forming the equipment; the second approach is using historical data to compute a statistical MTBF. The second approach is usually more accurate, as it considers implicitly all the factors that influence the equipment (asset) during its normal operations. Reliability can be represented in a time based scale as:
where n is the number of products and σ is a static constant of the manufacturing system. From a financial perspective, the maintenance procedures have an associated cost, and depending on how the business process is designed result indirectly in a profit increase by preventing resource break-down and allowing thus the completion of more customer orders. The maintenance cost associated with the resource break-down rate is:
where µ is a constant that depends on the manufacturing system. The profit for n products can be defined as:
where is the average revenue for a product, is the average production cost for a product (including all the materials, storage and other associated costs) and is the maintenance cost as defined above. The second probability expression considered in the business process is the probability of an order to be completed or accepted for scheduling. This probability can be determined from the history of executed commands, and highly depends on the HMES architecture, including but not limited to the number of redundant resources and the scheduling algorithm used. Resource redundancy may be total (identical resources) or partial (several resources may perform the same operation) The notation represents the scheduling probability associated with n products. The profit formula becomes: .
where t is the time representing the current time and f(x) is the probability density function. Based on the above mathematical representation, we can also define reliability as the probability that the equipment will operate in normal conditions for the time interval considered. One can also represent the reliability from an operational perspective, based on the number of products, assuming an average number of operations per product:
where n is the number of products considered and f'(x) is the probability density function from an operational perspective.
The third probability expression considered is the ratio of products that are produced but are not compliant with the QA policies associated to that product type. QA is implemented in the manufacturing system itself, using specific inspection techniques like online automated visual inspection (geometry, surface) (Borangiu 2004). This ratio is influenced by various factors including the CNC machines in the manufacturing process, the QA policies, the environment factors, etc. From a process perspective, when a customer order results in
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executing products that are not compliant with QA policies, the process starts again with the first step, by retrying to complete the original product order. This means that the resource availability is evaluated and then the scheduling is attempted in the same original delivery date. From a cost perspective, this is a negative scenario, as the costs are accumulated without reaching a profit. (n) is used to represent the probability that products will fail the QA tests and consequently will be rejected. Based on the above definitions, one can state that the probability to obtain a given profit for a batch of n products is a function that depends on these three probability functions:
specifically for processes with low predictability. A process is said to be predictable, if a historical analysis on the process shows a process variation that is between certain limits. This will assure that the process will behave in those limits in the future as well. One can evaluate this function and thus the variation of the average profit based on the variation in resource breakdown and QA acceptance rate either by historical analysis or/and by process simulation. A historical analysis has the advantage that it works with real data that considers all the real life factors implicitly (the path of thick arrows in Fig. 1). Process simulation has the advantage of simulating process changes and predicting the results of the changes on different levels. 4. THE COM BUSINESS PROCESS
where is the combined probability function, representing generically the probability that a product order cannot be completed for various reasons. The above formula is still not complete, because the process considered has an additional cost driven by the products rejected by QA step. The cost generated by QA failures can be represented as:
where is the QA failures rate. The overall mathematical expression of the profit including the QA loop back is:
The business process described is illustrated in Fig. 4. From a financial perspective, the process activities are of two types. The first type refers to the activities that have no relevant financial impact associated, because the underlying work involves no costs and revenue (e.g. a database call to retrieve the product holon associated to a customer order). The second type refers to activities having associated cost or revenue (e.g. is associated with an activity in the process). For simplicity reasons we have considered three activities that are financially relevant: HMES Execution: represents the actual manufacturing process in the HMES. This activity has an associated cost per product , represented as normal distribution function of costs across products; the computing parameters were set to Mean Value=100 and Standard Deviation=10.
The composed probability function above is a property of the business process and usually cannot be computed easily,
Fig. 4. The COM Business Process
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Depending on the nature of the products and the variation of their types, other distributions might be more appropriate to model the variation of the production costs. From a process perspective, this activity can be seen as a sub process, allowing a more precise evaluation of the costs at HMES level. Product Delivery: represents the activity to which the finished product, that has passed QA tests, is delivered to the customer. This activity has a revenue associated, represented by , calculated also as a normal distribution, with Mean Value=150 and the same Standard Deviation=10. Retrieve Resource Status: represents the process of interrogating the resource holons, based on product holon information. This task has the cost associated to represent to maintenance costs. The three process branches corresponding to the probabilities described are: Resources Available?: corresponds to the probability that required resources are not available and thus the order cannot be accepted. Scheduling Possible?: corresponds to the probability of order scheduling. If an order cannot be scheduled in the due delivery date, it will cause the rejection of the product order. QA Passed?: corresponds to the probability associated with a product to fail the QA test. This would cause a loop back in the process, by trying to re-execute the product order. The connections between these activities together with the holons passed between activities are illustrated in Figure 4. 5. PROCESS SIMULATION AND RESULTS There are several established experimental design patterns (NIST 2002) available for simulating a business process depending on the objectives set: • Comparative objective: is suitable for a scenario in which there are several factors that affect the process output but the objective of the experiment is to see how the variation of one (the most important) factor is affecting the process output. This model gives a measure of the significance of the main factor considered. • Screening objective: is used when the main objective of the experiment is to determine the most important effects, rather than the less important ones. • Response Surface objective: is used when two or more important factors vary. The results of the experiment are a surface that shows the interaction of the important factors considered in regards to the process output.
passed to each activity task according to the process design. When a branch is encountered, based on the probability that was configured, the token will follow one of the two possible paths. When an activity with a cost is executed, the value of that cost will be recorded. Similarly the revenue of an activity is recorded when the token is passed to that activity. The profit is computed as difference between revenue and costs. The methodology to compute the profit variation based on the variation of the three probabilities described in this paper is: 1. Setup initial values for the costs and revenues. 2. Setup initial probabilities for two of the branches. 3. Execute several simulations by varying the probability ratio for one of the branches that is analysed. 4. Record the profit variation together with the variation of the probability from step 3. The fixed values were assumed for (average revenue), (average production costs) and (maintenance costs), with the following values: •
= 150 (revenue), = 100 (cost) both simulated as a normal distribution with the standard deviation of 10. • = 10 (cost) Each test was executed on a sample of 1000 product orders (represented by tokens). Using such a large batch of orders for each test was required because of the low accuracy of the tool used when applying the probability ratio on each branch in the process. As the tool cannot know the total number of incoming tokens and depends on the loop back, it has to make a pseudo random decision on one branch and then correct the percentage on the next token, until it matches the required probability. In this study, the accuracy of the profit measurement was the important aspect, so this conceptual limitation of the simulation platform was resolved by increasing the amount of product orders considered at each simulation, until a variation < 1 was obtained between similar runs. The next section presents the results for each variation. 5.1 Variation of resource breakdown rate (
In what has been assumed to be normal conditions, characterized by a scheduling possible probability = 0.8 and a QA compliance probability = 0.95 simulations have been run having a variation of = . (1 represents the situation in which there are always resources available to complete every product order).
For the experiment described in this paper, we have used comparative objective in three iterative steps, for each of the considered factors. The business process illustrated in Fig. 4 was designed using IBM Web Sphere Business Modeller Advanced 6.0 (Iyengar et al 2007). The tool offers the possibility to simulate process execution by defining probability values for each branch. At simulation time, a token is generated at the process input and
)
Fig. 5. Profit variation based on
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Fig. 5 presents the results obtained, in terms of profit variation. The above plot represents the scenario in which is kept constant and the variation was done only on . A second simulation was done by increasing exponentially the variable while was decreased. This is based on the assumption that all resources will break if there is no maintenance done and the costs of having a rate of 0 (considering all the factors) would tend to ∞. This simulation shows an inflexion point that represents the optimum rate considering the exponentially increasing , at which the profit is maximum. Fig. 6 illustrates the simulation results for this scenario:
Fig. 8. Profit variation based on 6. CONCLUSIONS In this paper we have described a formal method for business process analysis in the context of holonic manufacturing systems. The simulation results show that there is a specific point where the resource breakdown rate, in conjunction with the maintenance costs provides a maximum profit. Also, it is shown how a scheduling algorithm can be evaluated from a cost efficient point of view. Because of the implicit loop back process connection, the QA fail strongly influences the profit.
Fig. 6. Profit variation based on
As the business process is changing continuously in order to improve profit rates, simulation of these processes provides valuable data (the Business Analytics and Optimization BAO module in the layered SOA, Fig. 1) to decision making:
and
5.2 Variation of scheduling probability rate (
)
Based on the simulation results of variation, for the study of the scheduling probability variation, a fixed value has been chosen for , simulating the inflexion point found above: . Using this value, the variation of = simulated. The results are presented in Fig. 7:
has been
• Design of the physical HMES, related to a larger type range of product to be manufactured; total and partial redundancy can be defined using the financial estimation of COM study • Design of the switching mechanism for shop-floor control modes: (1) hierarchical or (2) heterachical: (2a) with simple next resource allocation; (2b) negotiated, with optimization for products in simultaneous execution. For this, periodic update of the resource state, time performance, QA related to types of operations is used to change the input to the cost analysis algorithm which is periodically run. REFERENCES
Fig. 7. Profit variation based on We can see that the scheduling probability has a linear impact on the profit. 5.3 Variation of QA failure rate (
)
Based on previous simulations, for variation, the following values have been fixed for the other probabilities: and . Fig. 8 shows the results obtained:
Borangiu, Th. (2011). IBM Service Oriented Technologies and Management for Smarter enterprise, Proceedings of the IEEE Int. Conf. ICSTCC, Sinaia, October 14-16, ISBN 978-973-621-322-9, pp. 97-102 Borangiu, Th., Răileanu S., Anton F., Parlea M., Berger T., Trentesaux, D. (2011). Product-driven automation in a service oriented manufacturing cell, Proc.of the Int. Conf. on Industrial Eng. and Systems Management, IESM’11, Metz, May 25-27, ISBN 978-2-9600532-3-4 Iyengar A., Jessani V., Chilanti M. (2007). WebSphere Business Integration Primer: Process Server, BPEL, SCA, and SOA , IBM Press, ISBN: 013224831X NIST/SEMATECH (2002). e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/ *** (2005). Real-World SOA: SOA Platform Case Studies, Forrester Research, Inc. *** (2000). A Guide to the Project Management Body of Knowledge PMBOK, ISBN 1880410230 *** (2007). Standards for manufacturing systems integration, ISA-95 & OAGIS White Paper Series, White Paper #2: OAGIS, ISA-95 and Related Manufacturing
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Cost Analysis for Service-oriented Holonic Manufacturing System Design Cristina Morariu, Octavian Morariu, Theodor Borangiu
University Politehnica of Bucharest, Dept. of Automation and Applied Informatics e-mail: {cristina.morariu, octavian.morariu, theodor.borangiu}@cimr.pub.ro Abstract: This paper presents a formal methodology to perform a cost analysis for the customer order management (COM) process in the context of holonic manufacturing systems. The business process described in the paper, centred on COM, interconnects reference architecture with several enhancements that help in the mapping of the financial relevant activities. The process is analysed from two perspectives, first to show how the resource break-down rate in the HMES influences the overall profit and secondly to show the relationship between the percentage of products that fail the quality assurance test and the overall profit. The process was simulated using a BPM tool and the results are discussed. Keywords: HMES design, SOA, business services, cost of quality, Business Process Management
1. INTRODUCTION The environment of the manufacturing industry is nowadays characterized by radical transformation, concerning value creation. Value is no longer created simply in the production process, but derived from the knowledge, innovation, and service embodied throughout the entire product life cycle and their relationship with customers. The fundamental change factors in manufacturing - customers, market conditions and environmental constraints - will determine the economic growth engines for the business of the future, based on: • Customized solutions: integrating capabilities through products, services, and information to meet individual customer requirements. • Environmental sustainability: minimizing waste, energy and resource utilization. • Market conditions: creating individual solutions in a costeffective way to sustain a profitable business enterprise. • Timeliness: instant delivery of service to customers-product development, production lead times, delivery/ servicing time lines, and investment horizons are compressed. Businesses in the manufacturing industry only succeed if they meet all three of these conditions. Manufacturing will compete on delivering customer value at more and more lower costs, which will require individual products to be designed, planned and executed for individual customers to meet individual needs. Competitive advantage will be determined by production to individual specifications, and competitive cost structures should be put in place in order to ensure that customization is commercially feasible. The ultimate goal will be the competitive "batch of one". Mass customization will entail business tailoring product functionality, design and service to satisfy individual customer requirements, but also making differentiated products at high speed and in high volumes to keep unit costs at a minimum. Therefore, manufacturing enterprises will have to improve flexibility through continuous innovation and shorter production runs that can respond with agility to
978-3-902661-98-2/12/$20.00 © 2012 IFAC
changing and more specialized customer requirements together with shorter product life cycles. Mass customization will require further changes within the business of manufacturing: • Information systems integrating at enterprise level business and technical processes, that can quickly identify customer specifications, provide pertinent cost analysis, turn them automatically into work orders, and pull systems throughout supply chains and product-driven fabrication processes that meet both high-dynamics customer demands and manufacturing requirements influenced by the work environment, i.e. on demand production. • Agile production facilities for product planning, scheduling, transport, processing, assembling and quality control that can be rapidly reconfigured rather than reprogrammed, to produce different products or support product variations with minimal changeover times. • Interoperability, through standard product platforms and easily interchangeable and compatible physical devices, information systems and communication interfaces. • Process-based quality control and certification systems. • Traceability through individualized tracking and management of feature-based material flows. • Lean, just-in-time supply chains and low-cost logistics infrastructure. • Customer service that cover the life cycle of products, from design to financing, after-sale service, maintenance, recycling and disposal. In this context, the service value creation model (VCM) at manufacturing enterprise level consists into using a Service Component Architecture for business process applications, based on entities which handle (ask for, discover, provide, monitor) services. In this componentization view, a service is a piece of software encapsulating the business / control logic or resource functionality of an entity that exhibits an individual competence and responds to a specific request to fulfil a local (product operation, verification) or global objective (batch production).
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INCOM 2012, May 23-25, 2012 Bucharest, Romania
Current research is focusing on ways to integrate and map the Manufacturing Execution System (MES) to Service Oriented Architecture (SOA) concepts (Borangiu 2011). Modern enterprises are transforming their business models to a SOA oriented model as described in Forrester research (Forrester Research 2005), in order to reduce costs and to formalize and optimize the existing business processes.
• Business or Enterprise Applications such as those found in ERP for financial reporting, order management, etc. • MES applications that coordinate production and integrate with the plant floor and with enterprise level applications. • Device layer applications such as distributed control system or supervisory control and data acquisition (SCADA) which are used to manage the device layer in manufacturing.
2. SOA IN MANUFACTURING
The PROSA reference architecture (Van Brussel et al. 1998) for holonic manufacturing was adopted for the Holonic MES (HMES) developed in the present research, extended with the auto-supply functionality; the basic holons, defined as order / supply holon (OH/SH), resource holon (RH), product holon (PH) are coordinated by an expertise holon (EH) in the centralized control mode (Borangiu et al. 2011).
Enterprise Service Composition Environment
The way how service orientation is used in manufacturing is shown in Fig. 1, which gives an architectural view of the 5 business layers separating the enterprise (corresponding to the ISA-95 Enterprise Domain Hierarchy) (ISA-95 2007): Portal / Dashboard layer: • Data Aggregation & Visualization • User / Role based interaction
The reported research is related to the service-oriented link between the Plant HMES Systems (HMES) and one of the composite business processes - Customer Order Management (COM), aiming at enterprise integration with respect to both regular batch orders and rush orders (Fig. 2).
Composite Business Processes
Customer Order Inventory & Asset Supplier Management Integration Management
Offer Request Management
Enterprise business
Business Services layer [WSDL wraps business applications]
…
Componentization
Activity Monitoring
4
Customer Order Management
2
3
HMES
Products
Fig. 2. COM integration with HMES Data collection BAO
ERP SCM, CRM
The customer order management module receives the product order from the customer (via the Customer Relationship Management - CRM system) and checks if the HMES can complete it in due time and with which cost, based on resource status and availability. In this context, the COM module, and more specifically the business process orchestrating the actions associated with the processing of the customer order, encapsulates some of the financially relevant aspects of manufacturing.
Service Registry, WIP Tracking
Integration layer: Enterprise Service Bus (ESB)
PLM EDW
1
Order completed
…
Service Enablement BPM
OH sequence
Customer Order
Business choreography of SOA
Master Data Management
Plant HMES Systems SCADA Systems
Fig. 1. Overall SOA for manufacturing The bottom layer consists of existing or legacy applications that provide the foundation for how the business’ data is used. These are the ‘mission critical’ applications that keep the business running on a day to day basis. The next layer provides the integration. In a SOA, the integration layer is realized with Enterprise Services Bus (ESB). The ESB layer provides security, transport, mediation and event services. It also can provide business metrics and a workflow or business process engine. The Business Services (middle) layer is an abstraction layer of services "fronting" the foundation IT systems. These services are what do the work within the SOA. These services are represented using Web Service Description Language (WSDL) that wraps the business applications. The Business Processes layer consists of business processes that are created by combining the services in the Business Services layer together to create composite applications. Composite applications are a new way to develop application within the SOA. The Portal / Dashboard layer consists of data aggregation and visualization. The enterprise as a whole can be represented as layers under the control of three basic types of applications:
In this paper we will propose a business process for COM, based on the main HMES processes: batch product planning, operation scheduling and resource allocation, fed with shop floor asset data (fabrication year, functions, cross-redundancy and Mean-Time-Between-Failures) and having associated computing models for cost functions like production cost, resource maintenance, quality control, product delivery and revenue. Based on this process a set of analyses are described, showing how the resource break-down rate is influencing the profit, how the used scheduling algorithm influences the profit variation and how the percentage of products that pass the quality test influences the overall profit. Answers are given to the questions: "What is the optimum percentage of resource break-downs considering a profit margin and the related maintenance costs?"; "How affects the scheduling algorithm the profit?"; "How affects the profit the percentage of products which don't pass the QA (Quality Assurance) tests?" Chapter 3 focuses on financial aspects used in the process analysis. Chapter 4 describes the business process proposed. In chapter 5 the experimental method for process analysis is described and the results are presented.
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3. FINANCIAL ASPECTS IN COST ANALYSIS The business process proposed for customer management order is represented in a simplified way in Fig. 3.
Maintainability (Mean-Time-To-Repair MTTR): this is the measure of the ability of a unit of equipment to be restored to its performance capability within a specified average time. The relation between reliability and maintainability is given by the impact of increased maintenance procedures on the shape of the probability density function. The assumption is that proactive maintenance procedures will decrease the failure probability and will thus increase the reliability of the resources to a certain degree. In the HMES context, resource break-down rate can be defined as a measure of reliability for individual critical resources.
Fig. 3. Customer Order Management process In the COM business process three checks are performed that can alter the paths followed by the process, thus influencing the overall profit generated by the system. The first check is related to availability of the physical resources required for completing the order, the second asks for the possibility to schedule the order in the due delivery date and the third one is done for quality assurance before the product is delivered. For each of these three process branches a probability can be defined and associated with each decision path that will be discussed from a cost / profit perspective. In order to analyse the costs associated with the physical resources involved in the manufacturing process, two metrics are defined (PMBOK 2000): Reliability (Mean-Time-Between-Failures MTBF): is the degree to which a unit of equipment is performing its intended function under specified conditions for a specified period of time. There are two approaches to compute reliability: the first predicts the MTBF using a mathematical approach, aggregating the MTBF of components forming the equipment; the second approach is using historical data to compute a statistical MTBF. The second approach is usually more accurate, as it considers implicitly all the factors that influence the equipment (asset) during its normal operations. Reliability can be represented in a time based scale as:
where n is the number of products and σ is a static constant of the manufacturing system. From a financial perspective, the maintenance procedures have an associated cost, and depending on how the business process is designed result indirectly in a profit increase by preventing resource break-down and allowing thus the completion of more customer orders. The maintenance cost associated with the resource break-down rate is:
where µ is a constant that depends on the manufacturing system. The profit for n products can be defined as:
where is the average revenue for a product, is the average production cost for a product (including all the materials, storage and other associated costs) and is the maintenance cost as defined above. The second probability expression considered in the business process is the probability of an order to be completed or accepted for scheduling. This probability can be determined from the history of executed commands, and highly depends on the HMES architecture, including but not limited to the number of redundant resources and the scheduling algorithm used. Resource redundancy may be total (identical resources) or partial (several resources may perform the same operation) The notation represents the scheduling probability associated with n products. The profit formula becomes: .
where t is the time representing the current time and f(x) is the probability density function. Based on the above mathematical representation, we can also define reliability as the probability that the equipment will operate in normal conditions for the time interval considered. One can also represent the reliability from an operational perspective, based on the number of products, assuming an average number of operations per product:
where n is the number of products considered and f'(x) is the probability density function from an operational perspective.
The third probability expression considered is the ratio of products that are produced but are not compliant with the QA policies associated to that product type. QA is implemented in the manufacturing system itself, using specific inspection techniques like online automated visual inspection (geometry, surface) (Borangiu 2004). This ratio is influenced by various factors including the CNC machines in the manufacturing process, the QA policies, the environment factors, etc. From a process perspective, when a customer order results in
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executing products that are not compliant with QA policies, the process starts again with the first step, by retrying to complete the original product order. This means that the resource availability is evaluated and then the scheduling is attempted in the same original delivery date. From a cost perspective, this is a negative scenario, as the costs are accumulated without reaching a profit. (n) is used to represent the probability that products will fail the QA tests and consequently will be rejected. Based on the above definitions, one can state that the probability to obtain a given profit for a batch of n products is a function that depends on these three probability functions:
specifically for processes with low predictability. A process is said to be predictable, if a historical analysis on the process shows a process variation that is between certain limits. This will assure that the process will behave in those limits in the future as well. One can evaluate this function and thus the variation of the average profit based on the variation in resource breakdown and QA acceptance rate either by historical analysis or/and by process simulation. A historical analysis has the advantage that it works with real data that considers all the real life factors implicitly (the path of thick arrows in Fig. 1). Process simulation has the advantage of simulating process changes and predicting the results of the changes on different levels. 4. THE COM BUSINESS PROCESS
where is the combined probability function, representing generically the probability that a product order cannot be completed for various reasons. The above formula is still not complete, because the process considered has an additional cost driven by the products rejected by QA step. The cost generated by QA failures can be represented as:
where is the QA failures rate. The overall mathematical expression of the profit including the QA loop back is:
The business process described is illustrated in Fig. 4. From a financial perspective, the process activities are of two types. The first type refers to the activities that have no relevant financial impact associated, because the underlying work involves no costs and revenue (e.g. a database call to retrieve the product holon associated to a customer order). The second type refers to activities having associated cost or revenue (e.g. is associated with an activity in the process). For simplicity reasons we have considered three activities that are financially relevant: HMES Execution: represents the actual manufacturing process in the HMES. This activity has an associated cost per product , represented as normal distribution function of costs across products; the computing parameters were set to Mean Value=100 and Standard Deviation=10.
The composed probability function above is a property of the business process and usually cannot be computed easily,
Fig. 4. The COM Business Process
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Depending on the nature of the products and the variation of their types, other distributions might be more appropriate to model the variation of the production costs. From a process perspective, this activity can be seen as a sub process, allowing a more precise evaluation of the costs at HMES level. Product Delivery: represents the activity to which the finished product, that has passed QA tests, is delivered to the customer. This activity has a revenue associated, represented by , calculated also as a normal distribution, with Mean Value=150 and the same Standard Deviation=10. Retrieve Resource Status: represents the process of interrogating the resource holons, based on product holon information. This task has the cost associated to represent to maintenance costs. The three process branches corresponding to the probabilities described are: Resources Available?: corresponds to the probability that required resources are not available and thus the order cannot be accepted. Scheduling Possible?: corresponds to the probability of order scheduling. If an order cannot be scheduled in the due delivery date, it will cause the rejection of the product order. QA Passed?: corresponds to the probability associated with a product to fail the QA test. This would cause a loop back in the process, by trying to re-execute the product order. The connections between these activities together with the holons passed between activities are illustrated in Figure 4. 5. PROCESS SIMULATION AND RESULTS There are several established experimental design patterns (NIST 2002) available for simulating a business process depending on the objectives set: • Comparative objective: is suitable for a scenario in which there are several factors that affect the process output but the objective of the experiment is to see how the variation of one (the most important) factor is affecting the process output. This model gives a measure of the significance of the main factor considered. • Screening objective: is used when the main objective of the experiment is to determine the most important effects, rather than the less important ones. • Response Surface objective: is used when two or more important factors vary. The results of the experiment are a surface that shows the interaction of the important factors considered in regards to the process output.
passed to each activity task according to the process design. When a branch is encountered, based on the probability that was configured, the token will follow one of the two possible paths. When an activity with a cost is executed, the value of that cost will be recorded. Similarly the revenue of an activity is recorded when the token is passed to that activity. The profit is computed as difference between revenue and costs. The methodology to compute the profit variation based on the variation of the three probabilities described in this paper is: 1. Setup initial values for the costs and revenues. 2. Setup initial probabilities for two of the branches. 3. Execute several simulations by varying the probability ratio for one of the branches that is analysed. 4. Record the profit variation together with the variation of the probability from step 3. The fixed values were assumed for (average revenue), (average production costs) and (maintenance costs), with the following values: •
= 150 (revenue), = 100 (cost) both simulated as a normal distribution with the standard deviation of 10. • = 10 (cost) Each test was executed on a sample of 1000 product orders (represented by tokens). Using such a large batch of orders for each test was required because of the low accuracy of the tool used when applying the probability ratio on each branch in the process. As the tool cannot know the total number of incoming tokens and depends on the loop back, it has to make a pseudo random decision on one branch and then correct the percentage on the next token, until it matches the required probability. In this study, the accuracy of the profit measurement was the important aspect, so this conceptual limitation of the simulation platform was resolved by increasing the amount of product orders considered at each simulation, until a variation < 1 was obtained between similar runs. The next section presents the results for each variation. 5.1 Variation of resource breakdown rate (
In what has been assumed to be normal conditions, characterized by a scheduling possible probability = 0.8 and a QA compliance probability = 0.95 simulations have been run having a variation of = . (1 represents the situation in which there are always resources available to complete every product order).
For the experiment described in this paper, we have used comparative objective in three iterative steps, for each of the considered factors. The business process illustrated in Fig. 4 was designed using IBM Web Sphere Business Modeller Advanced 6.0 (Iyengar et al 2007). The tool offers the possibility to simulate process execution by defining probability values for each branch. At simulation time, a token is generated at the process input and
)
Fig. 5. Profit variation based on
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Fig. 5 presents the results obtained, in terms of profit variation. The above plot represents the scenario in which is kept constant and the variation was done only on . A second simulation was done by increasing exponentially the variable while was decreased. This is based on the assumption that all resources will break if there is no maintenance done and the costs of having a rate of 0 (considering all the factors) would tend to ∞. This simulation shows an inflexion point that represents the optimum rate considering the exponentially increasing , at which the profit is maximum. Fig. 6 illustrates the simulation results for this scenario:
Fig. 8. Profit variation based on 6. CONCLUSIONS In this paper we have described a formal method for business process analysis in the context of holonic manufacturing systems. The simulation results show that there is a specific point where the resource breakdown rate, in conjunction with the maintenance costs provides a maximum profit. Also, it is shown how a scheduling algorithm can be evaluated from a cost efficient point of view. Because of the implicit loop back process connection, the QA fail strongly influences the profit.
Fig. 6. Profit variation based on
As the business process is changing continuously in order to improve profit rates, simulation of these processes provides valuable data (the Business Analytics and Optimization BAO module in the layered SOA, Fig. 1) to decision making:
and
5.2 Variation of scheduling probability rate (
)
Based on the simulation results of variation, for the study of the scheduling probability variation, a fixed value has been chosen for , simulating the inflexion point found above: . Using this value, the variation of = simulated. The results are presented in Fig. 7:
has been
• Design of the physical HMES, related to a larger type range of product to be manufactured; total and partial redundancy can be defined using the financial estimation of COM study • Design of the switching mechanism for shop-floor control modes: (1) hierarchical or (2) heterachical: (2a) with simple next resource allocation; (2b) negotiated, with optimization for products in simultaneous execution. For this, periodic update of the resource state, time performance, QA related to types of operations is used to change the input to the cost analysis algorithm which is periodically run. REFERENCES
Fig. 7. Profit variation based on We can see that the scheduling probability has a linear impact on the profit. 5.3 Variation of QA failure rate (
)
Based on previous simulations, for variation, the following values have been fixed for the other probabilities: and . Fig. 8 shows the results obtained:
Borangiu, Th. (2011). IBM Service Oriented Technologies and Management for Smarter enterprise, Proceedings of the IEEE Int. Conf. ICSTCC, Sinaia, October 14-16, ISBN 978-973-621-322-9, pp. 97-102 Borangiu, Th., Răileanu S., Anton F., Parlea M., Berger T., Trentesaux, D. (2011). Product-driven automation in a service oriented manufacturing cell, Proc.of the Int. Conf. on Industrial Eng. and Systems Management, IESM’11, Metz, May 25-27, ISBN 978-2-9600532-3-4 Iyengar A., Jessani V., Chilanti M. (2007). WebSphere Business Integration Primer: Process Server, BPEL, SCA, and SOA , IBM Press, ISBN: 013224831X NIST/SEMATECH (2002). e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/ *** (2005). Real-World SOA: SOA Platform Case Studies, Forrester Research, Inc. *** (2000). A Guide to the Project Management Body of Knowledge PMBOK, ISBN 1880410230 *** (2007). Standards for manufacturing systems integration, ISA-95 & OAGIS White Paper Series, White Paper #2: OAGIS, ISA-95 and Related Manufacturing
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